Patterns of alternative splicing in response to cold acclimation in fish
Bibliographic record
Abstract
Phenotypic plasticity is an important aspect of an organism's response to environmental change that often requires the modulation of gene expression. These changes in gene expression can be quantitative as a result of increases or decreases in the amounts of specific transcripts, or qualitative as a result of the expression of alternative transcripts from the same gene (e.g., via alternative splicing of pre-mRNAs). Although the role of quantitative changes in gene expression in phenotypic plasticity is well known, relatively few studies have examined the role of qualitative changes. Here, we use skeletal muscle RNA-seq data from Atlantic killifish (Fundulus heteroclitus), threespine stickleback (Gasterosteus aculeatus) and zebrafish (Danio rerio) to investigate the extent of qualitative changes in gene expression in response to cold. Fewer genes demonstrated alternative splicing than differential expression as a result of cold acclimation; however, differences in splicing were detected for between 426 and 866 genes depending on species, indicating that large numbers of qualitative changes in gene expression are associated with cold acclimation. Many of these alternatively spliced genes were also differentially expressed, and there was functional enrichment for involvement in muscle contraction among the genes demonstrating qualitative changes in response to cold acclimation. Additionally, there was a common group of 29 genes with cold-acclimation-mediated changes in splicing in all three species, suggesting that there may be a set of genes with expression patterns that respond qualitatively to prolonged cold temperatures across fishes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".